
import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         GPTNeoXForCausalLM,
         GPTNeoXForQuestionAnswering,
         GPTNeoXForSequenceClassification,
         GPTNeoXForTokenClassification,
         GPTNeoXModel,
     )



class    _snake_case :


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=13       ,	SCREAMING_SNAKE_CASE_=7       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=99       ,	SCREAMING_SNAKE_CASE_=64       ,	SCREAMING_SNAKE_CASE_=5       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=37       ,	SCREAMING_SNAKE_CASE_="gelu"       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=5_12       ,	SCREAMING_SNAKE_CASE_=16       ,	SCREAMING_SNAKE_CASE_=2       ,	SCREAMING_SNAKE_CASE_=0.0_2       ,	SCREAMING_SNAKE_CASE_=3       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=None       ,	):


   '''simple docstring'''




   lowercase__	: Optional[Any]     		=   parent
   lowercase__	: List[str]     		=   batch_size
   lowercase__	: List[Any]     		=   seq_length
   lowercase__	: Any     		=   is_training
   lowercase__	: Optional[int]     		=   use_input_mask
   lowercase__	: List[str]     		=   use_token_type_ids
   lowercase__	: List[Any]     		=   use_labels
   lowercase__	: List[str]     		=   vocab_size
   lowercase__	: List[str]     		=   hidden_size
   lowercase__	: List[str]     		=   num_hidden_layers
   lowercase__	: List[str]     		=   num_attention_heads
   lowercase__	: Optional[Any]     		=   intermediate_size
   lowercase__	: Any     		=   hidden_act
   lowercase__	: List[Any]     		=   hidden_dropout_prob
   lowercase__	: Optional[Any]     		=   attention_probs_dropout_prob
   lowercase__	: Optional[int]     		=   max_position_embeddings
   lowercase__	: Any     		=   type_vocab_size
   lowercase__	: Optional[Any]     		=   type_sequence_label_size
   lowercase__	: Optional[int]     		=   initializer_range
   lowercase__	: str     		=   num_labels
   lowercase__	: Optional[int]     		=   num_choices
   lowercase__	: Any     		=   scope
   lowercase__	: Optional[int]     		=   vocab_size - 1


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Any     		=   ids_tensor([self.batch_size, self.seq_length]       ,	self.vocab_size)

   lowercase__	: int     		=   None
   if self.use_input_mask:
     lowercase__	: List[str]     		=   random_attention_mask([self.batch_size, self.seq_length])

   lowercase__	: List[str]     		=   None
   if self.use_labels:
     lowercase__	: Any     		=   ids_tensor([self.batch_size, self.seq_length]       ,	self.num_labels)

   lowercase__	: Any     		=   self.get_config()

   return config, input_ids, input_mask, token_labels


 def   lowercase__  (       self):


   '''simple docstring'''




   return GPTNeoXConfig(
       vocab_size=self.vocab_size       ,	hidden_size=self.hidden_size       ,	num_hidden_layers=self.num_hidden_layers       ,	num_attention_heads=self.num_attention_heads       ,	intermediate_size=self.intermediate_size       ,	hidden_act=self.hidden_act       ,	hidden_dropout_prob=self.hidden_dropout_prob       ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,	max_position_embeddings=self.max_position_embeddings       ,	type_vocab_size=self.type_vocab_size       ,	is_decoder=__snake_case       ,	initializer_range=self.initializer_range       ,	pad_token_id=self.pad_token_id       ,	)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[Any]     		=   self.prepare_config_and_inputs()

   lowercase__	: List[str]     		=   True

   return config, input_ids, input_mask, token_labels


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Any     		=   GPTNeoXModel(config=__snake_case)
   model.to(__snake_case)
   model.eval()
   lowercase__	: Union[str, Any]     		=   model(__snake_case       ,	attention_mask=__snake_case)
   lowercase__	: Tuple     		=   model(__snake_case)
   self.parent.assertEqual(result.last_hidden_state.shape       ,	(self.batch_size, self.seq_length, self.hidden_size))


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: str     		=   True
   lowercase__	: List[Any]     		=   GPTNeoXModel(__snake_case)
   model.to(__snake_case)
   model.eval()
   lowercase__	: Any     		=   model(__snake_case       ,	attention_mask=__snake_case)
   self.parent.assertEqual(result.last_hidden_state.shape       ,	(self.batch_size, self.seq_length, self.hidden_size))


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Optional[Any]     		=   GPTNeoXForCausalLM(config=__snake_case)
   model.to(__snake_case)
   model.eval()
   lowercase__	: Optional[int]     		=   model(__snake_case       ,	attention_mask=__snake_case       ,	labels=__snake_case)
   self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.seq_length, self.vocab_size))


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Any     		=   self.num_labels
   lowercase__	: Union[str, Any]     		=   GPTNeoXForQuestionAnswering(__snake_case)
   model.to(__snake_case)
   model.eval()
   lowercase__	: int     		=   model(__snake_case       ,	attention_mask=__snake_case)
   self.parent.assertEqual(result.start_logits.shape       ,	(self.batch_size, self.seq_length))
   self.parent.assertEqual(result.end_logits.shape       ,	(self.batch_size, self.seq_length))


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Any     		=   self.num_labels
   lowercase__	: Any     		=   GPTNeoXForSequenceClassification(__snake_case)
   model.to(__snake_case)
   model.eval()
   lowercase__	: int     		=   ids_tensor([self.batch_size]       ,	self.type_sequence_label_size)
   lowercase__	: str     		=   model(__snake_case       ,	attention_mask=__snake_case       ,	labels=__snake_case)
   self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.num_labels))


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: str     		=   self.num_labels
   lowercase__	: Optional[int]     		=   GPTNeoXForTokenClassification(__snake_case)
   model.to(__snake_case)
   model.eval()
   lowercase__	: Dict     		=   model(__snake_case       ,	attention_mask=__snake_case       ,	labels=__snake_case)
   self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.seq_length, self.num_labels))


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: str     		=   True
   lowercase__	: Any     		=   GPTNeoXForCausalLM(config=__snake_case)
   model.to(__snake_case)
   model.eval()

   # first forward pass
   lowercase__	: Union[str, Any]     		=   model(__snake_case       ,	attention_mask=__snake_case       ,	use_cache=__snake_case)
   lowercase__	: Dict     		=   outputs.past_key_values

   # create hypothetical multiple next token and extent to next_input_ids
   lowercase__	: Union[str, Any]     		=   ids_tensor((self.batch_size, 3)       ,	config.vocab_size)
   lowercase__	: List[Any]     		=   ids_tensor((self.batch_size, 3)       ,	vocab_size=2)

   # append to next input_ids and
   lowercase__	: Any     		=   torch.cat([input_ids, next_tokens]       ,	dim=-1)
   lowercase__	: Tuple     		=   torch.cat([input_mask, next_mask]       ,	dim=-1)

   lowercase__	: int     		=   model(__snake_case       ,	attention_mask=__snake_case       ,	output_hidden_states=__snake_case)
   lowercase__	: Tuple     		=   output_from_no_past["""hidden_states"""][0]
   lowercase__	: Optional[int]     		=   model(
       __snake_case       ,	attention_mask=__snake_case       ,	past_key_values=__snake_case       ,	output_hidden_states=__snake_case       ,	)["""hidden_states"""][0]

   # select random slice
   lowercase__	: str     		=   ids_tensor((1,)       ,	output_from_past.shape[-1]).item()
   lowercase__	: str     		=   output_from_no_past[:, -3:, random_slice_idx].detach()
   lowercase__	: Tuple     		=   output_from_past[:, :, random_slice_idx].detach()

   self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

   # test that outputs are equal for slice
   self.parent.assertTrue(torch.allclose(__snake_case       ,	__snake_case       ,	atol=1E-3))



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   self.prepare_config_and_inputs()
   lowercase__	: Optional[Any]     		=   config_and_inputs
   lowercase__	: List[Any]     		=   {"""input_ids""": input_ids, """attention_mask""": input_mask}
   return config, inputs_dict



@require_torch
class    _snake_case (						_snake_case	,    _snake_case	,    _snake_case	,    unittest.TestCase				):
 __lowerCAmelCase	:  Union[str, Any]	   =      (
     (
         GPTNeoXModel,
         GPTNeoXForCausalLM,
         GPTNeoXForQuestionAnswering,
         GPTNeoXForSequenceClassification,
         GPTNeoXForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 __lowerCAmelCase	:  Dict	   =      (GPTNeoXForCausalLM,) if is_torch_available() else ()
 __lowerCAmelCase	:  Dict	   =      (
     {
         """feature-extraction""": GPTNeoXModel,
         """question-answering""": GPTNeoXForQuestionAnswering,
         """text-classification""": GPTNeoXForSequenceClassification,
         """text-generation""": GPTNeoXForCausalLM,
         """token-classification""": GPTNeoXForTokenClassification,
         """zero-shot""": GPTNeoXForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __lowerCAmelCase	:  str	   =      False
 __lowerCAmelCase	:  Optional[int]	   =      False
 __lowerCAmelCase	:  Optional[int]	   =      False
 __lowerCAmelCase	:  Union[str, Any]	   =      False


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   GPTNeoXModelTester(self)
   lowercase__	: int     		=   ConfigTester(self       ,	config_class=__snake_case       ,	hidden_size=64       ,	num_attention_heads=8)


 def   lowercase__  (       self):


   '''simple docstring'''




   self.config_tester.run_common_tests()


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(__snake_case       ,	__snake_case       ,	__snake_case)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   self.model_tester.prepare_config_and_inputs_for_decoder()
   self.model_tester.create_and_check_model_as_decoder(__snake_case       ,	__snake_case       ,	__snake_case)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   self.model_tester.prepare_config_and_inputs_for_decoder()

   lowercase__	: Tuple     		=   None

   self.model_tester.create_and_check_model_as_decoder(__snake_case       ,	__snake_case       ,	__snake_case)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: int     		=   self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_decoder_model_past_large_inputs(__snake_case       ,	__snake_case       ,	__snake_case)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_causal_lm(*__snake_case)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Dict     		=   self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_question_answering(*__snake_case)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_sequence_classification(*__snake_case)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Tuple     		=   self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*__snake_case)


 @unittest.skip(reason="""Feed forward chunking is not implemented""")
 def   lowercase__  (       self):


   '''simple docstring'''




   pass



 @parameterized.expand([("""linear""",), ("""dynamic""",)])
 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: str     		=   self.model_tester.prepare_config_and_inputs_for_common()
   lowercase__	: Optional[Any]     		=   ids_tensor([1, 10]       ,	config.vocab_size)
   lowercase__	: int     		=   ids_tensor([1, int(config.max_position_embeddings * 1.5)]       ,	config.vocab_size)

   set_seed(42)  # Fixed seed at init time so the two models get the same random weights
   lowercase__	: List[str]     		=   GPTNeoXModel(__snake_case)
   original_model.to(__snake_case)
   original_model.eval()
   lowercase__	: List[str]     		=   original_model(__snake_case).last_hidden_state
   lowercase__	: Optional[int]     		=   original_model(__snake_case).last_hidden_state

   set_seed(42)  # Fixed seed at init time so the two models get the same random weights
   lowercase__	: Optional[int]     		=   {"""type""": scaling_type, """factor""": 1_0.0}
   lowercase__	: Optional[Any]     		=   GPTNeoXModel(__snake_case)
   scaled_model.to(__snake_case)
   scaled_model.eval()
   lowercase__	: Tuple     		=   scaled_model(__snake_case).last_hidden_state
   lowercase__	: Optional[int]     		=   scaled_model(__snake_case).last_hidden_state

   # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
   # maximum sequence length, so the outputs for the short input should match.
   if scaling_type == "dynamic":
     self.assertTrue(torch.allclose(__snake_case       ,	__snake_case       ,	atol=1E-5))
   else:
     self.assertFalse(torch.allclose(__snake_case       ,	__snake_case       ,	atol=1E-5))

   # The output should be different for long inputs
   self.assertFalse(torch.allclose(__snake_case       ,	__snake_case       ,	atol=1E-5))



@require_torch
class    _snake_case (						unittest.TestCase				):


 @slow
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Tuple     		=   AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped""")
   for checkpointing in [True, False]:
     lowercase__	: Any     		=   GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped""")

     if checkpointing:
       model.gradient_checkpointing_enable()
     else:
       model.gradient_checkpointing_disable()
     model.to(__snake_case)

     lowercase__	: Tuple     		=   tokenizer("""My favorite food is"""       ,	return_tensors="""pt""").to(__snake_case)
     # The hub repo. is updated on 2023-04-04, resulting in poor outputs.
     # See: https://github.com/huggingface/transformers/pull/24193
     lowercase__	: Optional[Any]     		=   """My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"""

     lowercase__	: int     		=   model.generate(**__snake_case       ,	do_sample=__snake_case       ,	max_new_tokens=20)
     lowercase__	: Dict     		=   tokenizer.batch_decode(__snake_case)[0]

     self.assertEqual(__snake_case       ,	__snake_case)










'''simple docstring'''


def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if b == 0:
								return 1
				if (b % 2) == 0:
								return actual_power(SCREAMING_SNAKE_CASE__				, int(b / 2	)	) * actual_power(SCREAMING_SNAKE_CASE__				, int(b / 2	)	)
				else:
								return a * actual_power(SCREAMING_SNAKE_CASE__				, int(b / 2	)	) * actual_power(SCREAMING_SNAKE_CASE__				, int(b / 2	)	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if b < 0:
								return 1 / actual_power(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
				return actual_power(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
				print(power(-2, -3))




